The multi-objective multigraph Shortest Path Problem (SPP) is intractable, necessitating efficient solution approaches. To address general multi-objective multigraph SPPs, this article introduces a Multi-Objective Multi-Graph A* (MOMGA*) algorithm and develops a learning-based heuristic function to expedite the search. MOMGA* generalises the Airport Multi-Objective A* (AMOA*), which was designed for a specific application on multigraphs, and further modifies its path selection and expansion procedures. Theoretical analysis demonstrates that the modifications in MOMGA* yield advantages over AMOA*, including higher search efficiency, more effective use of admissible heuristics for accelerating search, and seamless integration with likely-admissible heuristics without sacrificing solution quality. The admissibility proof of MOMGA* is also provided. The developed heuristic function is likely-admissible. It embraces node embedding techniques to extract node characteristics, based on which shortest path costs (heuristics) for every two nodes are estimated through neural networks. In particular, we present an extensive review of walk-based shallow embedding methods and experimentally validate their superior ability in capturing the characteristics of nodes for accurately predicting heuristics. Evaluation based on randomly generated multi-objective multigraphs confirms: (i) MOMGA* comprehensively outperforms AMOA*, consistent with the theoretical analysis; (ii) walk-based sampling for node embeddings is key to preserving distance-related information in graphs; (iii) the proposed likely-admissible heuristics, even learnt with a limited amount of training data, can empower MOMGA* to efficiently obtain a collection of optimal and near-optimal solutions; and (iv) a good balance between optimality and tractability in MOMGA* is controllable by tuning the predictive accuracy of learning heuristics.

Deep learning (DL) has shown state-of-the-art performance in trajectory prediction, which is critical to safe navigation in autonomous driving (AD). However, most DL-based methods suffer from catastrophic forgetting, where adapting to a new distribution may cause significant performance degradation in previously learned ones. Such inability to retain learned knowledge limits their applicability in the real world, where AD systems need to operate across varying scenarios with dynamic distributions. As revealed by neuroscience, the hippocampal circuit plays a crucial role in memory replay, effectively reconstructing learned knowledge based on limited resources. Inspired by this, we propose a hippocampal circuit-inspired continual learning method (H2C) for trajectory prediction across varying scenarios. H2C retains prior knowledge by selectively recalling a small subset of learned samples. First, two complementary strategies are developed to select the subset to represent learned knowledge. Specifically, one strategy maximizes inter-sample diversity to represent the distinctive knowledge, and the other estimates the overall knowledge by equiprobable sampling. Then, H2C updates via a memory replay loss function calculated by these selected samples to retain knowledge while learning new data. Experiments based on various scenarios from the INTERACTION dataset are designed to evaluate H2C. Experimental results show that H2C reduces catastrophic forgetting of DL baselines by 22.71% on average in a task-free manner, without relying on manually informed distributional shifts. The implementation is available at https://github.com/BIT-Jack/H2C-lifelong.

Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents’ predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic predictions. By leveraging MPPI’s gradient-free nature, we propose a method that efficiently approximates the joint Collision Probability (CP) among multiple dynamic obstacles for several hundred sampled trajectories in real-time via a Monte Carlo (MC) approach. This enables the rejection of samples exceeding a predefined CP threshold or the integration of CP as a weighted objective within the navigation cost function. Consequently, DRA-MPPI mitigates the freezing robot problem while enhancing safety. Real-world and simulated experiments with multiple dynamic obstacles demonstrate DRA-MPPI’s superior performance compared to state-of-the-art approaches, including Scenario-based Model Predictive Control (S-MPC), Frenét planner, and vanilla MPPI. Videos of the experiments can be found at https://autonomousrobots.nl/paper_websites/dra-mppi.

Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of traffic participants is essential to the decision-making and motion planning of AVs in interactive scenarios. Recently, learning-based trajectory predictors have shown state-of-the-art performance in highway or urban areas. However, most existing learning-based models trained with fixed datasets may perform poorly in continuously changing scenarios. Specifically, they may not perform well in learned scenarios after learning the new one. This phenomenon is called “catastrophic forgetting”. Few studies investigate trajectory predictions in continuous scenarios, where catastrophic forgetting may happen. To handle this problem, first, a novel continual learning (CL) approach for vehicle trajectory prediction is proposed in this paper. Then, inspired by brain science, a dynamic memory mechanism is developed by utilizing the measurement of traffic divergence between scenarios, which balances the performance and training efficiency of the proposed CL approach. Finally, datasets collected from different locations are used to design continual training and testing methods in experiments. Experimental results show that the proposed approach achieves consistently high prediction accuracy in continuous scenarios without re-training, which mitigates catastrophic forgetting compared to non-CL approaches. The implementation of the proposed approach is publicly available athttps://github.com/BIT-Jack/D-GSM.

Multitype satellite observation, including optical observation satellites, synthetic aperture radar (SAR) satellites, and electromagnetic satellites, has become an important direction in integrated satellite applications due to its ability to cope with various complex situations. In the multitype satellite observation scheduling problem (MTSOSP), the constraints involved in different types of satellites make the problem challenging. This article proposes a mixed-integer programming model and a generalized profit representation method in the model to effectively cope with the situation of multiple types of satellite observations. To obtain a suitable observation plan, a deep reinforcement learning-based genetic algorithm (DRL-GA) is proposed by combining the learning method and genetic algorithm. The DRL-GA adopts a solution generation method to obtain the initial population and assist with local search. In this method, a set of statistical indicators that consider resource utilization and task arrangement performance are regarded as states. By using deep neural networks to estimate the$Q$value of each action, this method can determine the preferred order of task scheduling. An individual update strategy and an elite strategy are used to enhance the search performance of DRL-GA. Simulation results verify that DRL-GA can effectively solve the MTSOSP and outperforms the state-of-the-art algorithms in several aspects. This work reveals the advantages of the proposed generalized model and scheduling method, which exhibit good scalability for various types of observation satellite scheduling problems.

Balancing safety and efficiency when planning in crowded scenarios with uncertain dynamics is challenging where it is imperative to accomplish the robot's mission without incurring any safety violations. Typically, chance constraints are incorporated into the planning problem to provide probabilistic safety guarantees by imposing an upper bound on the collision probability of the planned trajectory. Yet, this results in an overly conservative behavior on the grounds that the gap between the obtained risk and the specified upper limit is not explicitly restricted. To address this issue, we propose a real-time capable approach to quantify the risk associated with planned trajectories obtained from multiple probabilistic planners, running in parallel, with different upper bounds of the acceptable risk level. Based on the evaluated risk, the least conservative plan is selected provided that its associated risk is below a specified threshold. In such a way, the proposed approach provides probabilistic safety guarantees by attaining a closer bound to the specified risk, while being applicable to generic uncertainties of moving obstacles. We demonstrate the efficiency of our proposed approach, by improving the performance of a state-of-the-art probabilistic planner, in simulations and experiments using a mobile robot in an environment shared with humans.

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety, and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimization. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this article, we aim to develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimization algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong airport, we compared the developed algorithm with other well known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments cannot see through the cloud, the cloud coverage brings a significant influence on the satellite observation missions. The scheduling of multiple agile EOSs (AEOSs) is already complicated due to strong satellite maneuverability and onboard satellite energy constraints. Moreover, introducing cloud coverage uncertainty further increases scheduling complexity. Motivated by these challenges, we address the multiple AEOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. A chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via a sample approximation method. To overcome the solving difficulty, an improved simulated annealing (ISA)-based heuristic including a fast insertion strategy is proposed for large-scale observation missions. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method. Experimental results show that the ISA-based heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which validates the proposed algorithm.

Precisely modeling interactions and accurately predicting trajectories of surrounding vehicles are essential to the decision-making and path-planning of intelligent vehicles. This paper proposes a novel framework based on ensemble learning to improve the performance of trajectory predictions in interactive scenarios. The framework is termed Interactive Ensemble Trajectory Predictor (IETP). IETP assembles interaction-aware trajectory predictors as base learners to build an ensemble learner. Firstly, each base learner in IETP observes historical trajectories of vehicles in the scene. Then each base learner handles interactions between vehicles to predict trajectories. Finally, an ensemble learner is built to predict trajectories by applying two ensemble strategies on the predictions from all base learners. Predictions generated by the ensemble learner are final outputs of IETP. In this study, three experiments using different data are conducted based on the NGSIM dataset. Experimental results show that IETP improves the predicting accuracy and decreases the variance of errors compared to base learners. In addition, IETP exceeds baseline models with 50% of the training data, indicating that IETP is data-efficient. Moreover, the implementation of IETP is publicly available at https://github.com/BIT-Jack/IETP.

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The collision probability is calculated by summing up the probabilities of the states where two vehicles spatially overlap. Simulation results show that the prediction model has superior performance in terms of vehicle motion position errors, and the proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile, learning-based lane-change and trajectory prediction models have shown potential to provide accurate prediction results. We therefore propose a prediction-based driving risk metric for two-dimensional motion on multi-lane highways, expressed by the maximum risk value over different time instants within a prediction horizon. At each time instant, the risk of the vehicle is estimated as the sum of weighted risks over each mode in a finite set of lane-change maneuver possibilities. Under each maneuver mode, the risk is calculated as the product of three factors: lane-change maneuver mode probability, collision probability and expected crash severity. The three factors are estimated leveraging two-stage multi-modal trajectory predictions for surrounding vehicles: first a lane-change intention prediction module is invoked to provide lane-change maneuver mode possibilities, and then the mode possibilities are used as partial input for a multi-modal trajectory prediction module. Working with the empirical trajectory dataset highD and simulated highway scenarios, the proposed two-stage model achieves superior performance compared to a state-of-the-art prediction model. The proposed risk metric is computationally efficient for real-time applications, and effective to identify potential crashes earlier thanks to the employed prediction model.